Top Machine Learning Engineer Jobs in San Francisco Bay Area, CA

Build, fine-tune, deploy, and optimize production machine learning systems. Design scalable training, inference, evaluation, monitoring, and data pipelines; integrate models with enterprise systems and APIs; improve performance, reliability, latency, throughput, and cost efficiency; and collaborate with research, product, and engineering teams to deliver end-to-end AI features.

Lead design and implementation of post-training alignment pipelines (SFT, RLHF, RLAIF) using proprietary medical data. Improve model calibration, safety, and clinical correctness via novel techniques, distributed training on GPU clusters, and rigorous evaluation frameworks. Collaborate with co-founders to define research roadmap and ship production-ready ML systems.

Train and fine-tune multi-task transformer models for autonomous driving using JAX/Flax on TPUs. Build data and evaluation pipelines, conduct rigorous model evaluations, contribute production-quality code, and communicate findings through design documents and presentations. Collaborate with engineering, data science, and evaluation stakeholders.

Leads the technical direction and end-to-end delivery of machine learning and LLM systems for merchant quality, integrity, relevance, and shopping discovery. Responsibilities include architecture, implementation, evaluation, experimentation, monitoring, safety, scalability, and production rollout. The role establishes ML engineering standards, partners cross-functionally, mentors engineers, influences technical roadmaps, and supports future hiring while improving user trust and shopping outcomes.

Build evaluation infrastructure for Aircall’s AI voice, chat, and messaging agents. Responsibilities include designing benchmarks and metrics, training and fine-tuning TTS, ASR, and speech-to-speech models, developing automated regression and release gates, calibrating LLM-as-judge systems, analyzing failures, monitoring production quality and drift, and evaluating voice-specific scenarios such as accents, noise, barge-in, DTMF, latency, and tool failures.

Develop multimodal AI systems for image, video, generative AI, and virtual try-on applications. Optimize diffusion models for controllability and efficiency, design visual evaluation frameworks, productionize research prototypes, and collaborate with infrastructure teams to improve model training and inference.

Lead end-to-end LLM inference performance across serving systems, models, GPU hardware, and workloads. Define rigorous benchmarks, diagnose bottlenecks from scheduling through kernels and interconnects, optimize single- and multi-node deployments, and deliver production-ready runtimes and configurations. Collaborate with product and infrastructure teams, evaluate inference technologies, and implement runtime fixes. The role requires strong Python engineering, production experience with serving engines such as vLLM or SGLang, GPU profiling, and knowledge of modern inference optimization techniques.

Design, deploy, optimize, and maintain advanced machine learning models for monetization applications. Translate AI research into scalable production systems, build data pipelines, improve model performance, collaborate with research, engineering, product, and design teams, and contribute to model monitoring, code reviews, and end-to-end problem ownership.

Optimize machine-learning inference for latency, throughput, and cost. The role profiles pipelines, improves kernels, batching, quantization, compilation, serving, and hardware utilization, while building serving and evaluation infrastructure. It partners with research and product teams to make models affordable at scale and treats compute cost as a core performance metric.

Design, build, and support high-throughput, low-latency LLM inference and data pipelines; collaborate with data scientists to post-train and evaluate models; implement scalable, production-ready systems with strong testing, monitoring, and performance focus; mentor engineers and iterate on architecture, reliability, and user-facing AI applications.

Design, implement, benchmark, and optimize CUDA-based GPU kernels and custom operators for on-vehicle ML inference. Build tooling for profiling and debugging, integrate kernels into the ML runtime, collaborate with compilers and architecture teams, and deliver high-performance, production-grade libraries meeting strict latency, throughput, and reliability targets.

Lead neural decoding and machine learning strategy for brain-computer interfaces, taking algorithms from research through real-time deployment in human clinical studies. Develop closed-loop decoders, calibration and adaptation methods, inference runtimes, and clinical deployment processes. Define data and evaluation standards, integrate models across software, firmware, hardware, and robotics, support regulated development, debug cross-functional issues, and mentor ML engineers.

As an AI Engineer, you will develop and optimize AI systems for legal tasks, work on integrating models, and establish evaluation frameworks to enhance decision-making capabilities in complex legal workflows.

Design, scale, and maintain robust machine learning systems for AI-powered physical machines. Help define coding standards and the technology stack, work across software and hardware, solve ambiguous problems, and mentor new hires. Relevant work may include object recognition, reinforcement learning, and deep neural networks in a fast-growing AI and robotics startup.

As a Principal Machine Learning Systems Engineer, you will lead the development and implementation of machine learning systems and solutions to enhance team collaboration and productivity across Atlassian's software products.

Develop and productionize machine learning systems for search, retrieval, ranking, recommendations, conversational experiences, and AI agents. Own projects across the ML lifecycle, build evaluation datasets and benchmarks, conduct offline and online evaluations, analyze model quality, and improve system performance. Design scalable architectures addressing reliability, latency, privacy, and cost. Collaborate across product and engineering teams, communicate technical decisions, mentor engineers, and contribute to ML best practices.

Own and architect the core ranking system end-to-end: model design, production reliability, automated retraining, bias mitigation, personalization, and cross-functional technical leadership. Mentor engineers, drive roadmap, and scale experimentation and ML infrastructure.

Build and maintain production ML systems: training and inference pipelines, model packaging and serving, feature pipelines, monitoring for drift and degradation, automated retraining, and reproducibility/versioning across data and models.

Develop and improve machine learning systems for autonomous driving perception. Responsibilities include creating scalable ML methods, analyzing real-world system behavior, diagnosing root causes, enabling distributed fine-tuning, preventing production regressions, monitoring deployed models, building automated debugging tools, and developing evaluation metrics and recipes. The role requires Python or C++, PyTorch or JAX, and at least three years of machine learning or computer vision experience.

Develop and own end-to-end machine learning models for Uber Freight’s marketplace, including cost prediction, booking probability, demand elasticity, pricing, matching, recommendations, and optimization. Partner with backend engineers to deploy models to production and ensure reliable performance. Apply predictive modeling, causal inference, constrained optimization, reinforcement learning, and marketplace design to solve complex logistics problems.

Build and operate production machine learning and backend systems for AI data and evaluation products. Own a business vertical end to end, from identifying opportunities and setting direction to delivering revenue-generating products. Partner with customers, product managers, and engineers; drive adoption, customer value, and execution quality. Apply machine learning across multimodal data, LLM evaluation, physical-world data, robotics, audio, video, and related domains.

Lead the technical strategy and architecture for foundation models powering search, recommendations, personalization, and conversational AI across Uber’s Mobility and Delivery products. Own cross-team initiatives involving retrieval, ranking, embeddings, and LLM assistants; guide build-versus-buy decisions; influence product strategy; deliver large-scale ML systems; and mentor senior engineers while connecting model improvements to business outcomes.

Defines the multi-year technical vision and architecture for foundation models supporting search, recommendations, personalization, and conversational AI. Leads high-impact ML platform initiatives across Mobility and Delivery, drives retrieval, ranking, and LLM system design, and makes build-versus-partner investment decisions. Provides principal-level technical leadership, mentors senior engineers, aligns executives across organizations, and connects model improvements to measurable business outcomes at global scale.

Lead machine learning engineering for fraud prevention and account integrity. Shape technical roadmaps, formulate problems, design and productionize large-scale ML systems, oversee model pipelines and system architecture, conduct A/B testing, and manage rollouts. Mentor and develop MLE talent while applying machine learning, statistics, and optimization techniques to real-world risk and trust challenges.

Build and scale machine learning and backend distributed systems for Uber AI Solutions. Own an AI product vertical end to end, driving technical direction, customer value, revenue, adoption, and market growth. Partner with product and business leaders, guide strategic customer relationships, make decisions in ambiguous environments, and build high-performing teams. The role focuses on production ML, GenAI and LLM systems, evaluation, multimodal data, and other applied AI domains.